code: def normalize(X, norm):
      """Normalize a set of square matrices, using corr, trace or determinant.
  
      Parameters
      ----------
      X : ndarray, shape (..., n, n)
          The set of square matrices, at least 2D ndarray. Matrices must be
          invertible for determinant-normalization.
  
      norm : {"corr", "trace", "determinant"}
          The type of normalization:
  
          * 'corr': normalized matrices are correlation matrices, with values in
            [-1, 1] and diagonal values equal to 1;
          * 'trace': trace of normalized matrices is 1;
          * 'determinant': determinant of normalized matrices is +/- 1.
  
      Returns
      -------
      Xn : ndarray, shape (..., n, n)
          The set of normalized matrices, same dimensions as X.
      """
      if not is_square(X):
          raise ValueError("Matrices must be square")
  
      if norm == "corr":
          stddev = np.sqrt(np.abs(np.diagonal(X, axis1=-2, axis2=-1)))
          denom = (
              np.expand_dims(stddev, axis=-2) * stddev[..., np.newaxis]
          )
      elif norm == "trace":
          denom = np.trace(X, axis1=-2, axis2=-1)
      elif norm == "determinant":
          denom = np.abs(np.linalg.det(X)) ** (1 / X.shape[-1])
      else:
          raise ValueError(f"{norm} is not a supported normalization")
  
      denom = np.expand_dims(
          denom, axis=tuple(range(denom.ndim, X.ndim))
      )
      Xn = X / denom
  
      if norm == "corr":
          np.clip(Xn, -1, 1, out=Xn)
  
      return Xn
  
imports: import warnings
  from functools import wraps
  import numpy
  from scipy.linalg import block_diag
  from scipy.stats import chi2
  from sklearn.covariance import oas, ledoit_wolf, fast_mcd
  from .distance import distance_mahalanobis
  from .test import is_square, is_real_type
label_desc: Use image node_id 20 for calling a global function with example usage: normalize(X, norm) and returns: Xn